The paper presents a novel approach for representing color and intensity of pixel neighborhoods in an image using a co-occurrence matrix. After analyzing the properties of the HSV color space, suitable weight functions have been suggested for estimating relative contribution of color and gray levels of an image pixel. The suggested weight values for a pixel and its neighbor are used to construc...

The retrieval using single feature has a certain limitation, which fails to comprehensively describe an image. Aiming at such retrieval defect, this paper proposes an image retrieval method integrating color and texture. Firstly, carry out image segmentation with uniformly-spaced method, and then extract color feature of each segmentation with weighting processing done; and then, extract textur...

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are based on texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cra...

Modeling of semantic space is a new and challenging research topic both in cognitive science and linguistics. Existing approaches can be classified into two different types according to how the calculation are done: either a word-by-word co-occurrence matrix or a word-by-context matrix (Riordan 2007). In this paper, we argue that the existing popular distributional semantic model (vector space ...

Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people’s trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional ma...

Two approaches to the characterization of three-dimensional (3-D) textures are presented: one based on gradient vectors and one on generalized co-occurrence matrices. They are investigated with the help of simulated data for their behavior in the presence of noise and for various values of the parameters they depend on. They are also applied to several medical volume images characterized by the...

Image textural analysis technology has been widely used in the design of automated defect detection systems. Because the presence of defects may change the textural features of an image, a reference image without defects can be compared with the test image to detect whether there are any defects. However, besides defects, the deflection of the input test image could also change its textural fea...

We define the notion of indifferent set with respect to a given class of {0, 1}-sequences. Roughly, for a set A in the class, a set of natural numbers I is indifferent for A with respect to the class if it does not matter how we change A at the positions in I: the new sequence continues to be in the given class. We are especially interested in studying those sets that are indifferent with respe...

The aim of the study in this research deals with the accurate texture classification and the image texture analysis has a voluminous errand prospective in real world applications. In this study, the texton co-occurrence matrix applied to the Broadatz database images that derive the template texton grid images and it undergoes to the discrete shearlet transform to decompose the image. The entrop...

In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original d...